import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
def add_layer(layoutname,inputs,in_size,out_size,activatuib_funaction=None,):
    with tf.name_scope(layoutname):
        with tf.name_scope('weights'):
            Weights=tf.Variable(tf.random_normal([in_size,out_size]),name='W')
            tf.summary.histogram(layoutname+'/Weights', Weights)
        with tf.name_scope('biases'):
            biases=tf.Variable(tf.zeros([1,out_size])+0.1,name='b')
            tf.summary.histogram(layoutname+'/biases', biases)
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b=tf.add(tf.matmul(inputs,Weights),biases)

        if activatuib_funaction is None:
            outputs=Wx_plus_b
        else :
            outputs=activatuib_funaction(Wx_plus_b)
        tf.summary.histogram(layoutname+'/outputs', outputs)
        return outputs


x_data=np.linspace(-1,1,300)[:,np.newaxis]
noise=np.random.normal(0,0.09,x_data.shape)
y_data=np.square(x_data)-0.05+noise
with tf.name_scope('inputs'):
    xs=tf.placeholder(tf.float32,[None,1],name="x_input")
    ys=tf.placeholder(tf.float32,[None,1],name="y_input")


l1=add_layer("first_layer",xs,1,10,activatuib_funaction=tf.nn.relu)
l2=add_layer("first_layer1",l1,10,10,activatuib_funaction=tf.nn.tanh)

predition=add_layer('output_layout',l2,10,1,activatuib_funaction=None)
with tf.name_scope('loss'):
    loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-predition),reduction_indices=[1]))
    tf.summary.scalar('loss123',loss)
with tf.name_scope('train'):
    train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init=tf.global_variables_initializer()

with tf.Session() as sess:
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)
    ax.scatter(x_data,y_data)
    plt.ion()
    plt.show()
    merged=tf.summary.merge_all()
    writer=tf.summary.FileWriter("logs/",sess.graph)
    sess.run(init)
    for train in range(1000):

        sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
        if train%10==0:
            result=sess.run(merged,feed_dict={xs:x_data,ys:y_data})
            writer.add_summary(result,train)
            try:
                ax.lines.remove(lines[0])
            except Exception:
                  pass
            print train,sess.run(loss,feed_dict={xs:x_data,ys:y_data})
            predition_value=sess.run(predition,feed_dict={xs:x_data})
            lines=ax.plot(x_data,predition_value,'r-',lw=5)
            plt.pause(0.1)
    plt.pause(100)